Identification of Transportation Barriers in Patient Portal Messages via Deep Semantic Embeddings and Clustering.

Abstract

Patient portals have been widely used by patients to enable timely communications with their providers via secure messaging for various issues including transportation barriers. The large volume of portal messages offers an invaluable opportunity for studying transportation barriers reported by patients. In this work, we explored the feasibility of cutting-edge deep learning techniques for identifying transportation issues mentioned in patient portal messages with deep semantic embeddings. The successful creation of annotated corpus and identification of 7 transportation issues showed the feasibility of this strategy. The developed annotated corpus could aid in developing an artificial intelligence tool to automatically identify transportation issues from millions of patient portal messages. The identified specific transportation issues and the analysis of patient demographics could shed light on how to reduce transportation gaps for patients.